{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()\n",
    "X = iris.data\n",
    "y = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 2)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=2)\n",
    "pca.fit(X)\n",
    "\n",
    "X_reduced = pca.transform(X)\n",
    "X_reduced.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#X_reduced\n",
    "#y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EahPUI3768Lcyc/PNoMkr1/+XjO0Nr+emon5w2/SpvDZ/Dltzc9hTUMDnK5YzasI4vMG6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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaa151d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.scatter(X_reduced[:, 0], X_reduced[:, 1], c=y)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.36158968, -0.08226889,  0.85657211,  0.35884393],\n",
       "       [ 0.65653988,  0.72971237, -0.1757674 , -0.07470647]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.362 x sepal length (cm) + -0.082 x sepal width (cm) + 0.857 x petal length (cm) + 0.359 x petal width (cm)\n",
      "0.657 x sepal length (cm) + 0.730 x sepal width (cm) + -0.176 x petal length (cm) + -0.075 x petal width (cm)\n"
     ]
    }
   ],
   "source": [
    "for component in pca.components_:\n",
    "    print(\" + \".join(\"%.3f x %s\" % (value, name)\n",
    "                     for value, name in zip(component, iris.feature_names)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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nquqJ7mt+F3jpwf3JPs9UlY9FPoC19E+2Wtct94DTgJ8AL+/aPgFs7V7fBnyie/0p+pdV\n6NG/ENqjXfulwM/oX57hMPrnBFxKf0PdC7yyG/ce4Ivd6x3Av3fjzwUe6NrfDny0e/37wM6BOj7T\nvX4HcGv3egPwtRHf+ZmV/rn7mMi2+prWtlP6O6nfBs5e6Z/3Sj7GuUyA5nY+cH1V/RigqmaSvBn4\nclX9dzfmn4HPD7znhu7528BLq2oGmEny4iSrur6vVdVegCT/BkwBM8BdVfXDbsxngL8dWO/1VfVM\nktv59TUiLgBeneTPu+XBucYvdM+3A+9fzJfXb5XnbKv0t6nWttOPA3dU1bdHjGuaob40hwPPDLUd\nxnOvhwPPvR7OLwfanhpof4ZfT4c9PdB+BPC/Y6z3KYCqero7u/fZWt5VVTfwm5797KeBQ2fpV1uG\nt9WmttMkf0f/r4RL5xrzu8I59aX5D+AvB+YMXw58HfiL7pIK0L865c0LXO+fJlmd5AjgrcDX6J/R\n+8dJXtGN2QjcMmI9dwAbkxyW5AVJzhox/v/4XZ+PbNdztlX6c89NbKdJ/gZYB7ytunmY32WG+hJU\n1U7gH4BvJtkFXFRV9wJ/37X9iP6fkv+4wFV/H/gi8AP684i3VtXP6M89Xt+tdwNw2Yj1fAr4H+Cn\nwL30N/z5fA/4ZZIfDf+jNMmnu+94aPcPqm0L/E5aQbNsq6+hje30BODD9G/g80C3bb5vgd+jKV4m\n4HkmyaXAOVX1jpWuRZqL2+nzl3vqktQQQ12SGuL0iyQ1xD11SWqIoS5JDTHUJakhhrokNcRQl6SG\nGOqS1JD/BwjZ7DRlpd9eAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xac48fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(0,2), pca.explained_variance_)\n",
    "plt.xticks(range(0,2), ['component 1', 'component2'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='linear',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "clf = SVC(kernel='linear')\n",
    "clf.fit(X_reduced, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from itertools import product\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_estimator(estimator, X, y):\n",
    "    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),\n",
    "                         np.arange(y_min, y_max, 0.1))\n",
    "\n",
    "    Z = estimator.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "\n",
    "    plt.plot()\n",
    "    plt.contourf(xx, yy, Z, alpha=0.4, cmap = plt.cm.RdYlBu)\n",
    "    plt.scatter(X[:, 0], X[:, 1], c=y,  cmap = plt.cm.brg)\n",
    "    plt.xlabel('Component1')\n",
    "    plt.ylabel('Component2')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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eGX3tIar3oBJs3g7DLjGqhqSEzAOwORWm3AOnD4Xd++DCG8AfpsjPYYdH7zT+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xacd6e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_estimator(clf, X_reduced, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
